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Record W3194698214 · doi:10.2217/fon-2021-0087

The Impact of the Host Intestinal Microbiome on Carcinogenesis and the Response to Chemotherapy

2021· review· en· W3194698214 on OpenAlexafffund
Jaime Wertman, Katherine A. Dunn, Ketan Kulkarni

Bibliographic record

VenueFuture Oncology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersIWK Health Centre
KeywordsMicrobiomeMedicineIntestinal MicrobiomeColorectal cancerDysbiosisAntibioticsCancerDiseaseFecal bacteriotherapyGut microbiomeProbioticInflammatory bowel diseaseImmunologyBioinformaticsInternal medicineBiologyMicrobiologyGeneticsBacteria

Abstract

fetched live from OpenAlex

The microbiome consists of all microbes present on and within the human body. An unbalanced, or 'dysbiotic' intestinal microbiome is associated with inflammatory bowel disease, diabetes and some cancer types. Drug treatment can alter the intestinal microbiome composition. Additionally, some chemotherapeutics interact with microbiome components, leading to changes in drug safety and/or efficacy. The intestinal microbiome is a modifiable target, using strategies such as antibiotic treatment, fecal microbial transplantation or probiotic administration. Understanding the impact of the microbiome on the safety and efficacy of cancer treatment may result in improved treatment outcome. The present review seeks to summarize relevant research and look to the future of cancer treatment, where the intestinal microbiome is recognized as an actionable treatment target.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.362
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2021
Admission routes2
Has abstractyes

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